Electronic device and method for generating representative data for training base station model

ABSTRACT

An electronic device includes a memory storing instructions, a transceiver configured to receive base station data, and at least one processor configured to execute the instructions to: divide the base station data into a plurality of pieces of base station data according to a first time unit; generate first data of the first time unit by superimposing the plurality of pieces of base station data on each other; divide the first data of the first time unit into a plurality of second time intervals, according to a second time interval unit; calculate at least one probability density function for each second time interval of the plurality of second time intervals; generate at least one first representative data by using respective probability density functions of the plurality of second time intervals; and train the base station model, based on the at least one first representative data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of InternationalApplication No. PCT/KR2023/006031, filed on May 3, 2023, which claimspriority to Korean Patent Application No. 10-2022-0055727, filed on May4, 2022, and to Korean Patent Application No. 10-2022-0133615, filed onOct. 17, 2022, in the Korean Intellectual Property Office, thedisclosures of which are incorporated by reference herein in theirentireties.

BACKGROUND 1. Field

The disclosure relates to communication systems, and more particularly,to a device for generating representative data for training a basestation model, and a method, performed by the device, of generatingrepresentative data.

2. Description of Related Art

A network digital twin is a technique that may be used to predict anoperating environment of an actual base station by using a simulator toreplicate an operation and/or a configuration state of the actual basestation. For example, when a new wireless communication algorithm isdeveloped, the performance of the developed algorithm may be evaluatedin a particular simulator environment such that an operating environmentof an actual base station may not be applied. In addition, a basestation operation optimization model of a network digital twin maycollect base station data and train a base station model withreinforcement learning by using the collected base station data as aninput.

A process of training a base station model with reinforcement learningmay include generating a network digital twin by collecting base stationdata, preprocessing the collected base station data, finding simulationparameters by using the preprocessed data, and training a base stationoperation optimization model by setting arbitrary parameters in thenetwork digital twin. In this case, a large amount of data may be usedfor preprocessing the collected base station data and finding thesimulation parameters, and thus, a large amount of computationalresources may be consumed.

SUMMARY

According to an aspect of the present disclosure, an electronic devicefor generating representative data for training a base station model isprovided. The electronic device includes a memory storing one or moreinstructions, a transceiver configured to receive base station data, andat least one processor. The at least one processor is configured toexecute the one or more instructions stored in the memory to divide thebase station data into a plurality of pieces of base station dataaccording to a first time unit. The at least one processor is configuredto execute the one or more instructions stored in the memory to generatefirst data of the first time unit by superimposing the plurality ofpieces of base station data on each other. The at least one processor isconfigured to execute the one or more instructions stored in the memoryto divide the first data of the first time unit into a plurality ofsecond time intervals, according to a second time interval unit. The atleast one processor is configured to execute the one or moreinstructions stored in the memory to calculate at least one probabilitydensity function for each second time interval of the plurality ofsecond time intervals. The at least one processor is configured toexecute the one or more instructions stored in the memory to generate atleast one first representative data by using respective probabilitydensity functions of the plurality of second time intervals. The atleast one processor is configured to execute the one or moreinstructions stored in the memory to train the base station model, basedon the at least one first representative data.

According to an aspect of the present disclosure, a method of generatingrepresentative data for training a base station model is provided. Themethod includes dividing base station data into a plurality of pieces ofbase station data according to a first time unit. The method furtherincludes generating first data of the first time unit by superimposingthe plurality of pieces of base station data on each other. The methodfurther includes dividing the first data of the first time unit into aplurality of second time intervals, according to a second time intervalunit. The method further includes calculating at least one probabilitydensity function for each second time interval of the plurality ofsecond time intervals. The method further includes generating at leastone first representative data by using respective probability densityfunctions of the plurality of second time intervals. The method furtherincludes training the base station model, based on the at least onefirst representative data.

According to an aspect of the present disclosure, a computer-readablemedium may include one or more pieces of program code. When executed byan electronic device, the one or more pieces of program code may executea method including dividing base station data into a plurality of piecesof base station data according to a preset first time unit, generatingfirst data of the first time unit by superimposing the plurality ofpieces of base station data on each other, calculating at least oneprobability density function for each preset second time interval bydividing the superimposed first data according to the second timeinterval unit, generating at least one piece of first representativedata by using the probability density function for each second timeinterval, and training the base station model, based on the generated atleast one piece of first representative data. The recording mediumdisclosed as a technical unit for achieving the above-describedtechnical object may store a program for executing at least one of themethods according to embodiments of the present disclosure.

Additional aspects will be set forth in part in the description whichfollows and, in part, will be apparent from the description, or may belearned by practice of the presented embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of certainembodiments of the present disclosure will be more apparent from thefollowing description taken in conjunction with the accompanyingdrawings, in which:

FIG. 1 is a block diagram illustrating a configuration of an electronicdevice, according to an embodiment;

FIG. 2 is an exemplary block diagram illustrating functions of anelectronic device, according to an embodiment;

FIG. 3 illustrates superimposed data generated by an electronic deviceusing base station data, according to an embodiment;

FIG. 4 illustrates representative data of a probability density functiongenerated by an electronic device, according to an embodiment;

FIG. 5 illustrates representative data generated in a cycle of one day,according to an embodiment;

FIG. 6 is an exemplary block diagram illustrating a function of traininga learning model, according to an embodiment;

FIG. 7 illustrates a process of outputting a reward value to train abase station model, according to an embodiment;

FIG. 8 is a flowchart illustrating a method, performed by an electronicdevice, of generating representative data for training a learning model,according to an embodiment; and

FIG. 9 is a flowchart illustrating a method of training a learningmodel, according to an embodiment.

DETAILED DESCRIPTION

The following description of embodiments of the present disclosure isdescribed in detail with reference to the accompanying drawings forthose of skill in the art to be able to perform the present disclosurewithout any difficulty. The present disclosure may, however, be embodiedin many different forms and should not be construed as being limited tothe embodiments of the present disclosure set forth herein. In addition,in order to clearly describe the present disclosure, portions that arenot relevant to the description of the present disclosure may beomitted, and similar reference numerals may be assigned to similarelements throughout the present disclosure.

Although the terms used in the present disclosure may be selected fromamong common terms that may be currently widely used in consideration oftheir function in the present disclosure, the terms may be differentaccording to an intention of those of ordinary skill in the art, aprecedent, and/or the advent of new technology. Also, in particularcases, the terms may be discretionally selected by the applicant of thepresent disclosure, in which case, the meaning of those terms may bedescribed in detail in the corresponding embodiment of the presentdisclosure. Therefore, the terms used herein may not be merelydesignations of the terms, but the terms may be defined based on themeaning of the terms and content throughout the present disclosure.

It is to be understood that a singular expression may also include theplural meaning as long as it is not inconsistent with the context. Allthe terms used herein, including technical and scientific terms, mayhave the same meanings as those generally understood by those of skillin the art.

Also, the terms described in the present disclosure, such as “ . . . er(or)”, “ . . . unit”, “ . . . module”, and the like, that denote a unitthat performs at least one function or operation may be implemented ashardware, software, and/or a combination thereof.

Throughout the present disclosure, when a part is referred to as being“connected to” another part, it may refer to the part being “directlyconnected to”, “physically connected to” the other part, and/or“electrically connected to” the other part through an interveningelement. In the present disclosure, the terms “transmit”, “receive”, and“communicate”, as well as derivatives thereof, may encompass both directand indirect communication. In addition, when a part is referred to as“including” or “comprising” a component, it may refer to that the partmay additionally include or comprise other components rather thanexcluding other components as long as there is no particular opposingrecitation.

Throughout the present disclosure, the expression “or” may be inclusiveand not exclusive, as long as there is no particular opposingrecitation. Thus, the expression “A or B” may refer to “A, B, or both”as long as it is not inconsistent with the context. In the presentdisclosure, the phrase “at least one of”, when used with a list ofitems, may refer to different combinations of one or more of the listeditems being used, and/or only one item in the list may be needed. Forexample, “at least one of: A, B, or C” may include any of the followingcombinations: A, B, C, A and B, A and C, B and C, or A and B and C.

The term “controller” may refer to any device, system or part thereofthat controls at least one operation. Such a controller may beimplemented in hardware, a combination of hardware and software, and/orfirmware. The functionality associated with any particular controllermay be centralized or distributed, whether locally or remotely.

Reference throughout the present disclosure to “one embodiment,” “anembodiment,” “an example embodiment,” or similar language may indicatethat a particular feature, structure, or characteristic described inconnection with the indicated embodiment is included in at least oneembodiment of the present solution. Thus, the phrases “in oneembodiment”, “in an embodiment,” “in an example embodiment,” and similarlanguage throughout this disclosure may, but do not necessarily, allrefer to the same embodiment.

It is to be understood that the specific order or hierarchy of blocks inthe processes/flowcharts disclosed are an illustration of exemplaryapproaches. Based upon design preferences, it is understood that thespecific order or hierarchy of blocks in the processes/flowcharts may berearranged. Further, some blocks may be combined or omitted. Theaccompanying claims present elements of the various blocks in a sampleorder, and are not meant to be limited to the specific order orhierarchy presented.

Various embodiments of the present disclosure described below may beimplemented and/or supported by one or more computer programs, which maybe produced from computer-readable program code and/or stored in acomputer-readable medium. In the present disclosure, the terms“application” and “program” may refer to one or more computer programs,software components, instruction sets, procedures, functions, objects,classes, instances, relevant data, which may be suitable for animplementation in computer-readable program code, or a part thereof. Theterm “computer-readable program code” may include various types ofcomputer code including source code, object code, and executable code.The term “computer-readable medium” may include various types of mediathat is accessible by a computer, such as read-only memory (ROM),random-access memory (RAM), a hard disk drive (HDD), a compact disc(CD), a digital video disc (DVD), or various types of memory.

Additionally or alternatively, a computer-readable storage medium may beprovided in the form of a non-transitory storage medium. The term“non-transitory storage medium” may refer to a tangible device, and mayexclude wired, wireless, optical, or other communication links thattransmit temporary electrical or other signals. In addition, the term“non-transitory storage medium” may not distinguish between a case inwhich data is stored in a storage medium semi-permanently and a case inwhich data is stored temporarily. For example, the non-transitorystorage medium may include a buffer in which data is temporarily stored.A computer-readable medium may include any available medium that may beaccessible by a computer, and may include a volatile or non-volatilemedium and/or a removable or non-removable medium. The computer-readablemedia may include media in which data may be permanently stored and/ormedia in which data may be stored and overwritten later, such as, butnot limited to, a rewritable optical disc or an erasable memory device.

According to an embodiment of the present disclosure, methods accordingto various embodiments of the present disclosure may be included in acomputer program product and then provided. The computer programproducts may be traded as commodities between sellers and buyers. Thecomputer program product may be distributed in the form of amachine-readable storage medium (e.g., a CD-ROM), and/or may bedistributed (e.g., downloaded or uploaded) online through an applicationstore (e.g., PlayStore™) and/or between two user devices (e.g., smartphones) directly. In a case of online distribution, at least a portionof the computer program product (e.g., a downloadable application) maybe temporarily stored in a machine-readable storage medium such as amanufacturer's server, an application store's server, and/or a memory ofa relay server.

Definitions of other particular words and phrases may be providedthroughout the present disclosure. Those of skill in the art shouldunderstand that in many, if not most instances, such definitions applyto prior as well as future uses of such defined words and phrases.

In the present disclosure, each component described hereinafter mayadditionally perform some or all of functions performed by anothercomponent, in addition to main functions of itself, and some of the mainfunctions of each component may be performed entirely by anothercomponent.

In the present disclosure, the term “machine learning” may refer to afield of artificial intelligence, and/or may refer to an algorithm forlearning and/or executing an action that may not be empirically definedin code, based on data.

In the present disclosure, the term “reinforcement learning” may referto a field of machine learning, and/or may refer to a method to beperformed by a defined agent in a certain environment to recognize thecurrent state and/or select an action or an action sequence that maymaximize a reward from among a plurality of selectable actions.

In the present disclosure, the term “learning model” may refer to anexample of a model for learning an action by using a reinforcementlearning algorithm, and may not be limited to a model using a particularreinforcement learning algorithm.

In the present disclosure, the term “base station model” may refer to anagent that may be a subject of reinforcement learning, and/or may referto a simulator that may determine an operation of a base station of anetwork digital twin. For example, the base station model may perform anoperation of maximizing a reward for an environment input as a result ofreinforcement learning.

In order to generate a network digital twin of a radio access network(RAN), it may be necessary to generate a simulator that may replicate anoperation of a base station and/or may replicate network environmentdata collected from an actual base station. In order to replicatenetwork environment data, base station data may be preprocessed, andsimulation parameters that may produce a simulation result substantiallysimilar to and/or the same as the base station data may be obtained. Asignificant number of computational resources may be required to performsuch a process.

A process of training a base station model with reinforcement learningmay include collecting base station data, preprocessing the collectedbase station data, and training a base station operation optimizationmodel by setting arbitrary parameters in a generated network digitaltwin by using the preprocessed data.

Therefore, an electronic device according to an embodiment of thepresent disclosure may have various effects including an effect ofreducing computational resource consumption by performing preprocessingto generate representative data by using base station data.Alternatively or additionally, the electronic device according to anembodiment of the present disclosure may have various effects includingan effect of stably maintaining and/or operating a network state bytraining a learning model by using preprocessed data.

Hereinafter, an electronic device for generating representative basestation data by using base station data and training a base stationmodel is described.

FIG. 1 is a block diagram illustrating a configuration of an electronicdevice, according to an embodiment.

Referring to FIG. 1 , the electronic device 100 may include a memory110, a processor 120, and a transceiver 130. The number and arrangementof components of the electronic device 100 shown in FIG. 1 are providedas an example. In practice, there may be additional components, fewercomponents, different components, or differently arranged componentsthan those shown in FIG. 1 . Furthermore, two or more components shownin FIG. 1 may be implemented within a single component, or a singlecomponent shown in FIG. 1 may be implemented as multiple, distributedcomponents. Alternatively or additionally, a set of (one or more)components shown in FIG. 1 may be integrated with each other, and/or maybe implemented as an integrated circuit, as software, and/or acombination of circuits and software.

For example, the electronic device 100 may include an input unit forreceiving, from a user, an input of a first time period, a second timeperiod, and the like for generating representative data. Alternativelyor additionally, the electronic device 100 may include an output unitfor outputting a learning result.

In an embodiment, the operation of the processor 120 described below maybe implemented as a software module stored in the memory 110. Forexample, the software module may be stored in the memory 110 andoperated by being executed by the processor 120.

The memory 110 may be electrically connected to the processor 120, andmay store commands and/or data related to operations of the componentsincluded in the electronic device 100. According to various embodimentsof the present disclosure, the memory 110 may store base station datainformation obtained by using the transceiver 130, representative datagenerated by using the base station data, instructions for operations ofa base station model, and the like.

According to an embodiment of the present disclosure, at least somemodules included in respective units that may perform conceptuallyclassified functions of a network digital twin simulator described belowand/or the electronic device 100 may be implemented as software modulesthat may be executable by the processor 120, and/or the memory 110 maystore instructions for executing such software modules.

The processor 120 may be electrically connected to the componentsincluded in the electronic device 100 to perform computations and/ordata processing for control and/or communications of the componentsincluded in the electronic device 100. According to an embodiment of thepresent disclosure, the processor 120 may load, into the memory 110, acommand and/or data received from at least one of the other componentsof the electronic device 100, process the command and/or data, and storeresulting data in the memory 110.

FIG. 1 illustrates the processor 120 as one processor 120 forconvenience of description only. However, the present disclosure is notlimited in this regard. For example, at least one conceptuallyclassified function of a learning model described below and/or theelectronic device 100 may be implemented as a plurality of processors.That is, the processor 120 may not operate as one processor 120.Alternatively or additionally a plurality of processors may beimplemented as separate hardware units configured to perform respectiveoperations. A detailed description of the operation of the processor 120is provided with reference to FIGS. 2 to 7 .

The transceiver 130 may support establishment of a wired and/or awireless communication channel between the electronic device 100 and anexternal electronic device (e.g., an external base station, a serverthat may control an external base station). Alternatively oradditionally, communication between the electronic device 100 and theexternal electronic device may be performed through the establishedcommunication channel. For example, the transceiver 130 may receive datafrom the external electronic device and/or may transmit data to theexternal electronic device, through wired and/or wireless communication.

In an embodiment, data received by the transceiver 130 may include basestation data related to an operating environment of a network digitaltwin to be used for reinforcement learning of a base station model. Forexample, the base station data may include a physical resource block(PRB) usage, an internet protocol (IP) throughput, a number of activeuser equipments (UEs), a downlink volume, and the like.

In an embodiment, the PRB usage may refer to a ratio of a number of used(e.g., occupied) PRBs to a total number of PRBs available for a certaintime period. Alternatively or additionally, the PRB usage may becontinuously (e.g., periodically, aperiodically) collected according tothe time period.

The actual base station data may refer to data that may be used toperform reinforcement learning according to the operation of a basestation model.

In an optional or additional embodiment, data received and/ortransmitted by the transceiver 130 may include other types of data. Thatis, the data communications performed by the transceiver 130 are notlimited thereto.

According to various embodiments of the present disclosure, thetransceiver 130 may include a wireless communication module (e.g., acellular communication module, a short-range wireless communicationmodule, a global navigation satellite system (GNSS) communicationmodule) and/or a wired communication module (e.g., a local area network(LAN) communication module, a power line communication module).Alternatively or additionally, the transceiver 130 may communicate withan external electronic device through a short-range communicationnetwork (e.g., Bluetooth™, Wireless-Fidelity (Wi-Fi) direct, InfraredData Association (IrDA)) and/or a long-range communication network(e.g., a cellular network, the Internet, or a computer network (e.g., aLAN, a wide area network (WAN))), by using the wireless and/or wiredcommunication module.

Hereinafter, functions of the electronic device 100 and an operation ofa unit performing the functions are described with reference to FIG. 2 .

FIG. 2 is an exemplary block diagram illustrating functions of anelectronic device, according to an embodiment. An electronic device 200of FIG. 2 may include or may be similar in many respects to theelectronic device 100 described above with reference to FIG. 1 , and mayinclude additional features not mentioned above.

Referring to FIG. 2 , the electronic device 200 may include a datasuperimposing unit 210, a weighting unit 220, a representative datagenerating unit 230, and a learning unit 240. However, the functionsincluded in the electronic device 200 are not limited thereto. That is,the number and arrangement of components of the electronic device 200shown in FIG. 2 are provided as an example. In practice, the electronicdevice 200 may omit some components, may include additional componentsthat perform other functions, and/or components may differently arrangedthan as shown in FIG. 2 . For example, the electronic device 200 mayomit the weighting unit 220 and include only the data superimposing unit210, the representative data generating unit 230, and the learning unit240.

For convenience of description, the learning unit 240 of FIG. 2 may bedescribed as operated by the processor 120 of FIG. 1 . However, thepresent disclosure is not limited thereto. For example, a learningmethod of the learning unit 240 may be stored in the memory 110 of FIG.1 to be performed according to a command of the processor 120.Alternatively or additionally, functions of the data superimposing unit210, the weighting unit 220, and the representative data generating unit230 may be performed by the operation of the processor 120.

For another example, the learning unit 240 may be implemented as aseparate external device. That is, the electronic device 200 maytransmit representative data to be input to the learning unit 240 and/orreceive data output from the learning unit 240, by using the transceiver130, for example.

According to an embodiment of the present disclosure, the datasuperimposing unit 210, the weighting unit 220, the representative datagenerating unit 230, and the learning unit 240, as shown in FIG. 2 , maybe provided to perform respective operations of the processor 120 ofFIG. 1 that may be classified by function. Alternatively oradditionally, operations of the respective units shown in FIG. 2 and/orthe simulator may be stored as software in the memory 110 of FIG. 1 ,and then executed by the processor 120 to perform the respectivefunctions. However, the present disclosure is not limited thereto.

In an optional or additional embodiment, the data superimposing unit210, the weighting unit 220, the representative data generating unit230, and the learning unit 240 may be implemented as one processor. Thatis, the data superimposing unit 210, the weighting unit 220, therepresentative data generating unit 230, and the learning unit 240 maybe implemented as a dedicated processor and/or a combination of softwareand a general-purpose processor, such as an application processor (AP),a central processing unit (CPU) or a graphics processing unit (GPU).

In another optional or additional embodiment, the data superimposingunit 210, the weighting unit 220, the representative data generatingunit 230, and the learning unit 240 may be implemented in a plurality ofprocessors. That is, the data superimposing unit 210, the weighting unit220, the representative data generating unit 230, and the learning unit240 may be implemented as a combination of dedicated processors and/or acombination of software and general-purpose processors, such as APs,CPUs or CPUs.

The data superimposing unit 210 may divide base station data, which maybe received through the transceiver 130, into a plurality of pieces ofbase station data, according to a preset first time unit. Alternativelyor additionally, the data superimposing unit 210 may superimpose theplurality of pieces of base station data on each other to generate firstdata of the first time unit. The preset first time unit may refer to apredetermined time duration, such as, for example, 24 hours.

The base station data may include data about an environment of a basestation. Alternatively or additionally, the base station data mayinclude temporally continuous data and/or may include data recorded atpreset time intervals. For example, the base station data may include aPRB usage, an IP throughput, a number of active UEs, a downlink volume,and the like.

Since base station data may be collected considering seasonalcharacteristics, base station data collected for several years may berequired for reinforcement learning of the learning unit 240,Consequently, a significant number of computational resources may berequired to preprocess the collected base station data, and, as aresult, preprocessing computations may be complicated.

In an embodiment, the data superimposing unit 210 may performpreprocessing for superimposing pieces of base station data collectedfor several months on each other, for example. In the superimposed dataobtained by the superimposing by the data superimposing unit 210, thepieces of base station data of the first time unit may be superimposedon each other in the first time unit, and thus, there may be variouseffects including an effect of reducing computational resources requiredto preprocess data used for reinforcement learning.

An example method, performed by the data superimposing unit 210, ofsuperimposing pieces of base station data on each other is describedwith reference to FIG. 3 .

In an embodiment, the weighting unit 220 may assign a weight to recentlyrecorded base station data from among the superimposed data generated bythe data superimposing unit 210 for a first time period.

Alternatively or additionally, in a case in which a representative datagenerating unit 230 performs random sampling on the superimposed data,the weighting unit 220 may assign weights to the recently recorded basestation data to adjust the number of samples to be obtained through therandom sampling, such that less samples may be obtained from earlierrecorded data.

For example, in a case in which data obtained through the transceiver130 includes a PRB usage recorded from day 1 to day 90, and the datasuperimposing unit 210 divides the received PRB usage according to aunit of 24 hours to generate superimposed data, the superimposed datafor 24 hours may include 90 pieces of data for each hour. That is, the90 pieces of data for each hour may consist of a plurality of pieces ofdata from day 1 to day 90.

In this case, the weighting unit 220 may set, to N, the number of randomsamples to be obtained from data recorded in a period between day 1 today 30, where N is a positive integer greater than zero. Alternativelyor additionally, the weighting unit 220 may set, to 1.1×N, the number ofrandom samples to be obtained from data recorded in a period between day31 to day 60. In such an example, the weighting unit 220 may set, to1.2×N, the number of random samples to be obtained from data recorded ina period between day 61 to day 90.

That is, the weighting unit 220, according to an embodiment of thepresent disclosure, may assign a weight to recently recorded basestation data by further increasing the number of samples to be obtained.

The number of samples and/or the periods set by the weighting unit 220are merely examples, and other setting values may be set according tothe received base station data, user settings, and/or design constraintsof the electronic device 200. For example, the weighting unit 220 mayset the first time period to 48 hours.

Alternatively or additionally, various known methods of adding a weightto recently recorded data other than the above-described method may beutilized. That is, the present disclosure is not limited in this regard.For example, in a case in which the representative data generating unit230 generates a histogram for randomly sampled data, weights may beassigned such that earlier sampled data may be assigned a lower weight.

According to an embodiment of the present disclosure, in a case in whichthe electronic device 200 does not include the weighting unit 220, theelectronic device 200 may set a same number of samples for thesuperimposed data regardless of time. That is, the superimposed data maycontain a same number of samples for each first time unit.

The representative data generating unit 230, according to an embodimentof the present disclosure, may generate first representative data, basedon the superimposed data generated by the data superimposing unit 210and/or the weights set by the weighting unit 220.

For example, the representative data generating unit 230 may divide thesuperimposed data in preset second time interval units, and maycalculate a probability density function for each second time interval.Alternatively or additionally, the representative data generating unit230 may generate at least one piece of first representative data for thefirst time period by using the probability density function for eachsecond time interval.

In an embodiment, the representative data generating unit 230 mayrandomly sample the first data divided according to the second timeinterval to generate a histogram for each second time interval.Alternatively or additionally, the representative data generating unit230 may calculate the probability density function for each second timeinterval for the histogram for each second time interval. In an optionalor additional embodiment, the representative data generating unit 230may generate at least one piece of second representative data for eachsecond time interval by using the probability density function for eachsecond time interval. In another optional or additional embodiment, therepresentative data generating unit 230 may generate at least one pieceof first representative data by connecting the at least one piece ofsecond representative data for each second time interval to each otheraccording to the second time interval.

The first representative data may include environmental data of a basestation for training the learning unit 240. Alternatively oradditionally, the first representative data may be generated by using amean deviation and/or a standard deviation of the probability densityfunction for each second time interval.

An example method, performed by the representative data generating unit230, of generating first representative data is described with referenceto FIG. 4 .

In an embodiment, the representative data generating unit 230 maygenerate a network digital twin including a base station model by usingthe generated first representative data. The generating of a networkdigital twin may refer to setting whether a function of a base stationmodel included in the network digital twin is activated, operatingparameters related to an operation of the base station model, and/or atleast one input parameter for replicating a commercial network basestation. That is, the representative data generating unit 230 may set atleast one parameter of the base station model included in the networkdigital twin in order to replicate an environment in which thecommercial network base station operates, by using the firstrepresentative data.

The operating parameters related to the operation of the base stationmodel may include at least one of a handover parameter, a selection orreselection parameter, a cell on/off parameter, and a load balancingparameter.

In an embodiment, the determining whether a function is activated mayinclude determining whether at least one of a scheduling algorithm, ahandover algorithm, and a discontinuous reception (DRX) algorithm isactivated.

The at least one input parameter for replicating the commercial networkbase station may indicate at least one of an average packet size, anaverage request interval, and a number of terminals.

The representative data generating unit 230 may generate a small numberof network digital twins by generating representative data from basestation data of a commercial network base station, and, thus, may havevarious effects including reducing computational resources.

A network digital twin generated by the representative data generatingunit 230 may be trained with reinforcement learning based on a result ofapplying various settings by the learning unit 240.

In order to train an operation of a base station model (e.g., basestation model 245 of FIG. 6 ) with reinforcement learning, the learningunit 240 may train the operation of the base station model 245 by usingthe first representative data, which may be related to a base stationenvironment and may be generated by the representative data generatingunit 230.

An example method, performed by the learning unit 240, of training theoperation of the base station model 245 by using the firstrepresentative data is described below with reference to FIGS. 6 and 7 .

FIG. 3 illustrates superimposed data generated by an electronic device200 using base station data, according to an embodiment.

Referring to FIGS. 2 and 3 , the base station data 310 obtained by theelectronic device 200 may include PRB usage and may have been recordedfor 7 days, for example. In an embodiment, the data superimposing unit210 may divide the obtained base station data 310 into 24-hour units(e.g., one-day units) to generate day-1 base station data 311, day-2base station data 312, day-3 base station data 313, day-4 base stationdata 314, day-5 base station data 315, day-6 base station data 316, andday-7 base station data 317. As shown in FIG. 3 , the x axis of theobtained base station data 310 may represent time (e.g., hour) from thefirst time point at which the base station data 310 is initiallymeasured (e.g., hour 0) to the last time point at which the base stationdata 310 is finally measured (e.g., hour 168), and the y axis mayrepresent PRB usage (in percentage (%) units).

In an embodiment, the PRB usage may show similar patterns every 24hours. Consequently, the data superimposing unit 210 may set the firsttime interval to 24 hours.

The data superimposing unit 210, according to an embodiment, maygenerate superimposed data 320 by superimposing the generated day-1 basestation data 311 to day-7 base station data 317 on each other into onegraph, in which the x axis may represent the first time interval (e.g.,24 hours or 0:00 to 24:00). The y axis of the superimposed data 320 mayrepresent the PRB usage in percentage units.

The superimposed data 320 may include data obtained by superimposing alldata from the day-1 base station data 311 to the day-7 base station data317 on each other into one piece of data spanning the first timeinterval (e.g., 24 hours), and may indicate a PRB usage for 7 days.

The superimposed data 320 generated by the data superimposing unit 210may be used by the representative data generating unit 230 to generatefirst representative data.

The representative data generating unit 230 may generate a histogram foreach second time interval by randomly sampling the base station data 310divided according to the preset second time interval. Alternatively oradditionally, the representative data generating unit 230 may calculatea probability density function for each second time interval withrespect to the histogram for each second time intervals.

It is to be understood that the base station data 310, the superimposeddata 320, and the generated day-1 base station data 311 to day-7 basestation data 317 illustrated in FIG. 3 are merely one example, and thatother embodiments may have different data and/or different time units.That is, the example illustrated in FIG. 3 is not intended to limit thescope of the present disclosure, but is merely representative of variousembodiments.

FIG. 4 illustrates representative data of a probability density functiongenerated by an electronic device, according to an embodiment.

Referring to FIGS. 2 to 4 , the x axis of the graph may present PRBusage in units of sampled percentages, and the y axis may represent aprobability density (e.g., solid line) having a value between zero (0)and one (1).

In an embodiment, the representative data generating unit 230 may setthe second time interval to 1 hour, and may perform random sampling on asection of the superimposed data 320 of FIG. 3 corresponding to a 4:00hour to generate a histogram and/or a probability density function for asampled PRB usage.

That is, assuming that the second time interval is set to 1 hour andrandom sampling is performed on data corresponding to the 4:00 section,the representative data generating unit 230 may randomly sample data ofthe time period between 3:30 and 4:30. For example, the representativedata generating unit 230 may randomly sample N pieces of data from thePRB usage data of the time period between 3:30 and 4:30 in FIG. 3 ,where N is a positive integer greater than zero (0).

By using the above-described method, the representative data generatingunit 230 may randomly sample data about the PRB usage for the 0:00section to the 24:00 section. In this case, the 0:00 section maycorrespond to a time period from 0:00 to 0:30, and the 24:00 section maycorrespond to a time period from 23:30 to 24:00.

Alternatively or additionally, the second time interval set by therepresentative data generating unit 230 may be 2 hours. In this case,the representative data generating unit 230 may randomly sample N piecesof data for each of the 0:00 section, the 2:00 section, the 4:00section, . . . , and the 24:00 section, where N is a positive integergreater than zero (0).

The representative data generating unit 230, according to an embodimentof the present disclosure, may perform random sampling such that, fromamong the first data divided according to the second time interval, asmaller number of samples are obtained from pieces of first data thatare earlier recorded, based on the first time unit.

For example, in a case in which the base station data 310 of FIG. 3includes data from day 1 to day 90, the first time unit is 24 hours, andthe second time unit is 1 hour, the superimposed data 320 of FIG. 3generated by the data superimposing unit 210 may include pieces of basestation data from day 1 to day 90 superimposed on each other in a timeperiod between 0:00 to 24:00. The representative data generating unit230 may sample the superimposed data 320 of FIG. 3 for each time ofgeneration, such that a smaller number of samples are obtained fromearlier recorded data, on a 24-hour basis (e.g., on a one-day basis).

In an embodiment, in a case of randomly sampling N samples, therepresentative data generating unit 230 may randomly sample N/6 samplesfrom the base station data 310 recorded between day 1 and day 30,randomly sample N/3 samples from the base station data 310 recordedbetween day 31 and day 60, and randomly sample N/2 samples from the basestation data 310 recorded between day 61 and day 90.

That is, the representative data generating unit 230 may assign a weightto recently recorded data by randomly sampling a larger number ofsamples from the recently recorded data. Consequently, therepresentative data generating unit 230 may train a base station model245 to be closer (e.g., more similar) to the latest operationenvironment of the base station data 310.

The representative data generating unit 230 may generate a histogrambased on randomly sampled data for each time interval, generate aprobability density function for each time interval from the generatedhistogram, and generate at least one piece of second representative datafor time interval from the generated probability density function foreach time interval.

The at least one piece of second representative data for each timeinterval may refer to data that may represent base station data 310obtained at each time interval. For example, the second representativedata may be a value calculated by using a mean p, a median, a maximum, aminimum, and/or a standard deviation a of the probability densityfunction. Alternatively or additionally, the second representative datamay include any value as long as the value may be used to represent thebase station data 310 at each time interval

According to an embodiment of the present disclosure, the at least onepiece of second representative data for each time interval may be μ−3σ,μ−2σ, μ+2σ, and μ+3σ. That is, the representative data generating unit230 may select, as the at least one piece of second representative data,μ±2σ, which may indicate upper and lower boundary values between whichthe base station data 310 may exist with a probability of approximately95%, and μ±3σ, which may indicate upper and lower boundary valuesbetween which the base station data 310 may exist with a probability ofapproximately 99%.

As the representative data generating unit 230 selects μ−3σ, μ−2σ, μ+2σ,and μ+3σ as the second representative data, the base station model 245may be trained by using, as representative data, boundary value datahaving a high probability that an actual environment exists. In otherwords, the base station model 245 may be trained by receiving the bestenvironment and the worst environment among actual base stationenvironments.

The representative data generating unit 230, according to an embodimentof the present disclosure, may generate at least one piece of firstrepresentative data by connecting the at least one piece of secondrepresentative data for each second time interval to each otheraccording to the second time interval. The first representative data mayinclude a value obtained by connecting pieces of representative data ofthe base station data 310 for a first time period. The firstrepresentative data may be used interchangeably with the term“compaction” in the present disclosure.

FIG. 5 illustrates representative data for a first time period,according to an embodiment of the present disclosure.

In FIG. 5 , the x axis may represent time (e.g., hours) from 0:00 to24:00, and the y axis may represent PRB usage measured in percentages.

Referring to FIGS. 2 and 5 , the representative data generating unit 230may indicate changes in first representative data during one day, whichmay be a first time unit, by connecting at least one piece of secondrepresentative data to each other to generate the first representativedata.

For example, in a case in which μ−3σ is selected as the secondrepresentative data, the representative data generating unit 230 maygenerate a first compaction 511 by connecting a piece of secondrepresentative data for 0:00 to a piece of second representative datafor 1:00 with a straight line, and connecting the piece of secondrepresentative data for 1:00 to a piece of second representative datafor 2:00.

By using the above-described method, a second compaction 513 may begenerated using μ−2σ as the second representative data, a thirdcompaction 515 may be generated using p as the second representativedata, a fourth compaction 517 may be generated using μ−2σ as the secondrepresentative data, and a fifth compaction 519 may be generated usingμ−3σ as the second representative data.

That is, the representative data generating unit 230 may generate atleast one piece of first representative data by connecting pieces ofrepresentative data of the base station data 310 for respective secondtime intervals within the first time unit to each other.

For example, in a case in which the base station data 310 includes a PRBusage, the at least one piece of first representative data may includeall representative situations, from the first compaction 511 in whichthe base station environment may not be good (e.g., high PRB usage), tothe fifth compaction 519 in which the base station environment may begood (e.g., low PRB usage), among the base station data 310 for 90 days.

In an embodiment, an electronic device 200 may train the base stationmodel 245 with reinforcement learning, by using a small amount of basestation data 310 by selecting representative data. That is, theelectronic device 200 may have various effects including an effect ofincreasing the rate of computations required for training as the numberof simulations for each operation decreases according to a small amountof base station data 310.

The learning unit 240, according to an embodiment of the presentdisclosure, may train a base station model 245 for a network digitaltwin by using the at least one piece of first representative datagenerated by the representative data generating unit 230.

FIG. 6 is an exemplary block diagram illustrating a function for alearning unit to train a base station model with reinforcement learning,according to an embodiment of the present disclosure.

Referring to FIG. 6 , the learning unit 240 may include a rewarddetermination unit 241, a reward determination unit 243 and the basestation model 245. Although FIG. 6 shows the base station model 245 asbeing included in the learning unit 240 for convenience of description,the present disclosure is not limited thereto. For example, the basestation model 245 may be stored in the memory 110 of FIG. 1 , and/or maybe stored in an external device (e.g., a server).

Reinforcement learning may be selected as a learning method of thelearning unit 240. Reinforcement learning may refer to a field ofmachine learning, and may refer to a method of learning actions that maybe optimal to perform in a current state. For example, in a learningmethod of reinforcement learning, a reward may be given by an externalenvironment whenever an agent takes an action, and learning may beperformed in order to maximize the reward.

The reward determination unit 241 may receive representative datagenerated by the representative data generating unit 230 and an actionperformed by the base station model 245 as an agent. Alternatively oradditionally, the reward determination unit 241 may calculate a rewardfor each second time interval according to each action and anenvironment corresponding to the representative data.

That is, the reward determination unit 241 may simulate an actionselectable by the base station model 245 for at least one piece of firstrepresentative data representing a base station environment, and/or maycalculate a reward for each action for each second time interval.

The reward determination unit 243 may transmit training data to the basestation model 245, based on the reward for each time interval calculatedby the reward determination unit 241. For example, the rewarddetermination unit 243 may provide the training data by using the sum ofminimum rewards for the respective time intervals, such that the basestation model 245 may not (e.g., be prevented from) perform an actioncorresponding to the minimum reward.

That is, the reward determination unit 243 may generate the trainingdata to train the base station model 245 such that the base stationmodel 245 may safely operate and/or avoid key performance indicator(KPI) degradation that may occur by performing each action for the basestation environment represented by the first representative data.Alternatively or additionally, the base station model 245 may operate ina manner that may maximize an energy saving.

The base station model 245, according to an embodiment of the presentdisclosure, may include a model obtained by replicating an actual basestation, for constructing a network digital twin. Alternatively oradditionally, the base station model 245 may be a subject of learning,that is, an agent of reinforcement learning that may perform an actionaccording to a reward.

In an embodiment, the base station model 245 may be preconfigured toperform functions similar to those of an actual base station, and/or mayreceive (e.g., from a user) an input of a plurality of actions of a basestation with various relevant setting values.

For example, actions of the base station model 245 may include, but notbe limited to, an action of turning on at least one cell and an actionof turning off the at least one cell, based on a PRB usage.

A process of training the base station model 245 with reinforcementlearning by using at least one piece of first representative data isdescribed with reference to FIG. 7 .

FIG. 7 illustrates a process of outputting a reward value to train abase station model, according to an embodiment.

Referring to FIGS. 6 and 7 , the reward determination unit 241 mayperform simulations such that the base station model 245 may perform anaction for each second time interval of the base station environments(e.g., first compaction 511, second compaction 513, third compaction515, fourth compaction 517, and fifth compaction 519). In an embodiment,the actions performed by the base station model 245 in the firstcompaction 511 to the fifth compaction 519 may correspond to rewardsthat may be defined as Rmax, R75p, Ravg, R25p, and Rmin, respectively.

The state of the base station model 245 may change from a particularstate of the base station model 245 according to the action performed bythe base station model 245. Accordingly, the reward determination unit241 may calculate a reward value according to a predefined rule, basedon the changed state of the base station model 245.

The predefined rule may include a rule for ensuring communicationperformance and/or maximizing a power saving. Alternatively oradditionally, the predefined rule may include a rule configured todecrease the reward as the power consumption increases. In anotherexample, the predefined rule may include a rule configured to set thereward to a negative value when the IP throughput is less than a presetvalue.

However, the present disclosure is not limited in this regard. That is,various known rules may be applied to the predefined rule according todesign constraints and/or the environment of the base station.

In an embodiment, the base station model 245 may perform actions in theenvironments of the first compaction 511 to the fifth compaction 519 foreach second time interval. Consequently, the reward determination unit241 may calculate the rewards for the respective time intervals tomaximize the power saving while ensuring the communication performance.That is, the reward determination unit 241 may select, as the reward forthe corresponding second time interval, the minimum reward from amongthe rewards for the first compaction 511 to the fifth compaction 519 foreach second time interval, such that KPI degradation may be minimized.

For example, at 3:00, in a case in which the reward Rmax for the firstcompaction 511 may be calculated to be 10, the reward R75p for thesecond compaction 513 may be calculated to be 7, the reward Ravg for thethird compaction 515 may be calculated to be 6, the reward R25p for thefourth compaction 517 may be calculated to be −1, and the reward Rminfor the fifth compaction 519 may be calculated to be 1, the rewarddetermination unit 241 may select a reward 703 of the fourth compaction517 (e.g., the minimum reward value), as the reward for 3:00.Alternatively or additionally, the reward determination unit 241 may, ina similar manner, calculate and select a reward 701 for 1:00, a reward702 for 2:00, a reward 704 for 4:00, a reward 705 for 5:00, a reward 706for 6:00, to a reward 707 for 24:00.

In an embodiment, the reward determination unit 243 may receive thereward for each second time interval from the reward determination unit241 and may calculate training data using the following equation:

$\begin{matrix}\left( {a,{{\sum}_{Z4}^{t = 1}\min\limits_{c \in C}R_{c}^{t}}} \right) & \left( {{Equation}1} \right)\end{matrix}$

Here, a may represent an action performed by the base station model 245,t may represent the second time interval, c may represent eachcompaction, and R_c{circumflex over ( )}t may represent a reward foreach compaction at each time interval.

That is, the reward determination unit 243 may transmit, to the basestation model 245, training data including the actions performed by thebase station model 245 and the sum of minimum values of the rewards forthe respective actions, to train the base station model 245 withreinforcement learning.

The base station model 245, according to an embodiment of the presentdisclosure, may be trained to perform an action that may maximize thereward by using the training data obtained from the reward determinationunit 243. Alternatively or additionally, the base station model 245 maybe trained not to perform an action with the lowest sum of calculatedreward values.

As such, the electronic device 200 may train the base station model 245with reinforcement learning to minimize KPI deterioration whilegenerating various effects including an energy saving effect.

Continuing to refer to FIG. 7 , exemplary configuration and functions ofthe electronic device 200 for generating base station representativedata for training a base station model 245 have been described.Hereinafter, an example method, performed by the electronic device 200,of generating representative data for training a base station model 245is described with respect to FIG. 8 .

FIG. 8 is a flowchart illustrating an example method, performed by anelectronic device, of generating representative data for training a basestation model, according to an embodiment.

Referring to FIGS. 2 and 8 , the electronic device 200 may generatefirst data of a preset first time unit by superimposing at least onepiece of obtained base station data 310 on each other into the firsttime unit (operation S810).

The base station data 310, according to an embodiment of the presentdisclosure, may include at least one of a PRB usage, an IP throughput, anumber of active UEs, and a downlink volume.

In an embodiment, the PRB usage may refer to a ratio of a number of used(e.g., occupied) PRBs to a total number of PRBs available for a certaintime period. Alternatively or additionally, the PRB usage may becontinuously (e.g., periodically, aperiodically) collected according tothe time period.

The electronic device 200 may calculate at least one probability densityfunction for each preset second time interval by dividing thesuperimposed first data according to the second time interval unit(operation S820).

For example, the electronic device 200 may generate a histogram for eachsecond time interval by randomly sampling the base station data 310divided according to the preset second time interval. Alternatively oradditionally, the electronic device 200 may calculate a probabilitydensity function for each second time interval with respect to thehistogram for each second time intervals.

The electronic device 200 may generate at least one piece of firstrepresentative data by using the calculated at least one probabilitydensity function for each second time interval (operation S830).

In an embodiment, the electronic device 200 may randomly sample thefirst data divided according to the second time interval to generate ahistogram for each second time interval. Alternatively or additionally,the electronic device 200 may calculate a probability density functionfor each second time interval for the histogram for each second timeinterval. In an optional or additional embodiment, the electronic device200 may generate at least one piece of second representative data foreach second time interval by using the probability density function foreach second time interval. In another optional or additional embodiment,the electronic device 200 may generate at least one piece of firstrepresentative data by connecting the at least one piece of secondrepresentative data for each second time interval to each otheraccording to the second time interval.

The first representative data, according to an embodiment of the presentdisclosure, may include environmental data of a base station fortraining a base station model 245. Alternatively or additionally, thefirst representative data may have been generated by using a meandeviation and/or a standard deviation of the probability densityfunction for each second time interval.

The electronic device 200 may train the base station model 245 based onthe generated at least one piece of first representative data (operationS840).

In an embodiment, the base station model 245 may include a modelobtained by replicating an actual base station, for constructing anetwork digital twin. Alternatively or additionally, the base stationmodel 245 may be a subject of learning. That is, the base station model245 may be an agent of reinforcement learning that may perform an actionaccording to a reward.

In an embodiment, the base station model 245 may be preconfigured toperform functions similar to those of an actual base stationAlternatively or additionally, the base station model 245 may receive(e.g., from a user) an input of a plurality of actions of a base stationwith various relevant setting values.

FIG. 9 is a flowchart illustrating an example method of training a basestation model, according to an embodiment.

Referring to FIGS. 2 and 9 , the electronic device 200 may output areward value for each piece of first representative data for each secondtime interval by applying at least one action of the base station model245 to an environment of the generated at least one piece ofrepresentative data (operation S910).

In an embodiment, the actions of the base station model 245 may includean action of turning on at least one cell and/or an action of turningoff the at least one cell, based on a PRB usage.

Alternatively or additionally, the state of the base station model 245may change from a particular state according to the action of the basestation model 245. Accordingly, a reward value may be may calculatedaccording to a predefined rule, based on the changed state of the basestation model 245.

The predefined rule may include a rule for ensuring communicationperformance and/or maximizing a power saving. Alternatively oradditionally, the predefined rule may include a rule configured todecrease the reward as the power consumption increases. In anotherexample, the predefined rule may include a rule configured to set thereward to a negative value when the IP throughput is less than a presetvalue.

However, the present disclosure is not limited in this regard. That is,various known rules may be applied to the predefined rule according todesign constraints and/or the environment of the base station.

Alternatively or additionally, the electronic device 200 may receive aninput of representative data and an action of the base station model 245as an agent. Consequently, the electronic device 200 may output a rewardfor each second time interval according to the action and an environmentcorresponding to the representative data.

The electronic device 200 may select a representative reward value foreach second time interval from among the output reward values (operationS920).

In an embodiment, the lowest reward value for each second time intervalamong the at least one piece of first representative data may beselected as the representative reward value for each second timeinterval. Accordingly, the electronic device 200 may calculate therepresentative reward for each second time interval to maximize a powersaving while ensuring communication performance.

The electronic device 200 may calculate the sum of the selectedrepresentative reward values for each second time interval, and trainthe base station model 245 not to perform the action having the lowestsum of calculated reward values, among the at least one action(operation S930).

In an embodiment, the electronic device 200 for generatingrepresentative data for training a base station model 245 may include amemory 110 storing one or more instructions, a transceiver 130, and atleast one processor 120 configured to execute the one or moreinstructions stored in the memory 110. When the instructions areexecuted, the at least one processor 120 may divide base station data310 received through the transceiver 130 into a plurality of pieces ofbase station data according to a preset first time unit, and generatefirst data of the first time unit by superimposing the plurality ofpieces of base station data on each other. When the instructions arefurther executed, the at least one processor 120 may calculate at leastone probability density function for each preset second time interval bydividing the superimposed first data according to the second timeinterval unit. When the instructions are further executed, the at leastone processor 120 may generate at least one piece of firstrepresentative data by using the at least one probability densityfunction for each second time interval. When the instructions arefurther executed, the at least one processor 120 may train the basestation model 245, based on the generated at least one piece of firstrepresentative data.

In an embodiment, the base station data 310 may include at least one ofa PRB usage, an IP throughput, a number of active UEs, and a downlinkvolume.

In an embodiment, when the instructions are further executed, the atleast one processor 120 may generate a histogram for each second timeinterval by performing random sampling on the first data dividedaccording to the second time interval. When the instructions are furtherexecuted, the at least one processor 120 may calculate a probabilitydensity function for each second time interval for the histogram foreach second time interval. When the instructions are further executed,the at least one processor 120 may select at least one piece of secondrepresentative data for each second time interval by using theprobability density function for each second time interval. When theinstructions are further executed, the at least one processor 120 maygenerate the at least one piece of first representative data byconnecting the at least one piece of second representative data for eachsecond time interval to each other according to the second timeinterval.

In an embodiment, when the instructions are further executed, the atleast one processor 120 may perform the random sampling such that, fromamong the first data divided according to the second time interval, asmaller number of samples are obtained from pieces of first data thatare earlier recorded, based on the first time unit.

In an embodiment, when the instructions are further executed, the atleast one processor 120 may select the at least one piece of secondrepresentative data for each second time interval by using a mean andstandard deviation of the probability density function for each secondtime interval.

In an embodiment, when the instructions are further executed, the atleast one processor 120 may generate a network digital twin includingthe base station model 245 by setting, based on the generated at leastone piece of representative data, at least one parameter of the networkdigital twin. When the instructions are further executed, the at leastone processor 120 may output a reward value for each of the at least onepiece of first representative data for each second time interval byapplying at least one action of the base station model 245 to anenvironment of the generated at least one piece of representative data.When the instructions are further executed, the at least one processor120 may select a representative reward value for each second timeinterval from among the output reward values. When the instructions arefurther executed, the at least one processor 120 may train the basestation model 245 not to perform an action having the lowest sum ofcalculated reward values, among the at least one action, by calculatingthe sum of the selected representative reward values for each secondtime interval.

In an embodiment, when the instructions are further executed, the atleast one processor 120 may select, as the representative reward valuefor each second time interval, a lowest reward value for each secondtime interval, from among the output reward values.

In an embodiment, the at least one action of the base station model 245may include at least one of an action of turning on at least one celland an action of turning off the at least one cell, based on a PRBusage.

In an embodiment, the action of turning on the at least one cell mayinclude, in a case in which the IP throughput and/or the PRB usage of acertain frequency band is greater than or equal to a preset first value,turning on at least one cell of the frequency band. In an optional oradditional embodiment, the action of turning off the at least one cellmay include, in a case in which the IP throughput and/or the PRB usageof a certain frequency band is less than or equal to a preset secondvalue, turning off at least one cell of the frequency band.

A method of generating representative data for training a base stationmodel 245, according to an embodiment of the present disclosure, mayinclude dividing base station data 310 into a plurality of pieces ofbase station data according to a preset first time unit, generatingfirst data of the first time unit by superimposing the plurality ofpieces of base station data on each other, calculating at least oneprobability density function for each preset second time interval bydividing the superimposed first data according to the second timeinterval unit, generating at least one piece of first representativedata by using the probability density function for each second timeinterval, and training the base station model 245, based on thegenerated at least one piece of first representative data.

In an embodiment, the base station data 310 may include at least one ofa PRB usage, an IP throughput, a number of active UEs, and a downlinkvolume.

In an embodiment, the calculating of the at least one probabilitydensity function may include generating a histogram for each second timeinterval by randomly sampling the first data divided according to thesecond time interval, and calculating a probability density function foreach second time interval for the histogram for each second timeinterval. In an embodiment, the generating of the at least one piece offirst representative data may include generating at least one piece ofsecond representative data for each second time interval by using theprobability density function for each second time interval, andgenerating the at least one piece of first representative data byconnecting the at least one piece of second representative data for eachsecond time interval to each other according to the second timeinterval.

In an embodiment, the generating of the histogram for each second timeinterval may include performing the random sampling such that, fromamong the first data divided according to the second time interval, asmaller number of samples are obtained from pieces of first data thatare earlier recorded, based on the first time unit.

In an embodiment, the generating of the at least one piece of secondrepresentative data may include generating the at least one piece ofsecond representative data for each second time interval, by using amean and a standard deviation of the probability density function foreach second time interval.

In an embodiment, the training of the base station model 245 may includegenerating a network digital twin including the base station model 245by setting, based on the generated at least one piece of representativedata, at least one parameter of the network digital twin, outputting areward value for each of the at least one piece of first representativedata for each second time interval by applying at least one action ofthe base station model 245 to an environment of the generated at leastone piece of representative data, selecting a representative rewardvalue for each second time interval from among the output reward values,and training the base station model 245 not to perform an action havingthe lowest sum of calculated reward values, among the at least oneaction, by calculating the sum of the selected representative rewardvalues for each second time interval.

In an embodiment, the selecting of the representative reward value foreach second time interval may include selecting, as the representativereward value for each second time interval, a lowest reward value foreach second time interval, from among the output reward values.

In an embodiment, the at least one action of the base station model 245may include at least one of an action of turning on at least one celland an action of turning off the at least one cell, based on an IPthroughput and/or a PRB usage.

In an embodiment, the action of turning on the at least one cell mayinclude, in a case in which the IP throughput and/or the PRB usage of acertain frequency band is greater than or equal to a preset first value,turning on at least one cell of the frequency band. In an optional oradditional embodiment, the action of turning off the at least one cellmay include, in a case in which the IP throughput or the PRB usage of acertain frequency band is less than or equal to a preset second value,turning off at least one cell of the frequency band.

As a technical unit for achieving the above-described technical object,a computer-readable medium may include one or more pieces of programcode. When executed by an electronic device (e.g., electronic device 100of FIG. 1 , electronic device 200 of FIG. 2 ), the one or more pieces ofprogram code may execute a method including dividing base station data310 into a plurality of pieces of base station data according to apreset first time unit, generating first data of the first time unit bysuperimposing the plurality of pieces of base station data on eachother, calculating at least one probability density function for eachpreset second time interval by dividing the superimposed first dataaccording to the second time interval unit, generating at least onepiece of first representative data by using the probability densityfunction for each second time interval, and training the base stationmodel 245, based on the generated at least one piece of firstrepresentative data.

The recording medium disclosed as a technical unit for achieving theabove-described technical object may store a program for executing atleast one of the methods according to embodiments of the presentdisclosure.

The machine-readable storage medium may be provided in the form of anon-transitory storage medium. The term “non-transitory storage medium”may refer to a tangible device and may not include a signal (e.g., anelectromagnetic wave), and the term “non-transitory storage medium” maynot distinguish between a case where data is stored in a storage mediumsemi-permanently and a case where data may be stored temporarily. Forexample, the non-transitory storage medium may include a buffer in whichdata is temporarily stored.

The previous description is provided to enable a person skilled in theart to practice the various aspects described herein. Variousmodifications to these aspects are to be readily apparent to thoseskilled in the art, and the generic principles defined herein may beapplied to other aspects. Thus, the claims are not intended to belimited to the aspects shown herein, but are to be accorded the fullscope consistent with the language of the claims.

What is claimed is:
 1. An electronic device for generatingrepresentative data for training a base station model, the electronicdevice comprising: a memory storing one or more instructions; atransceiver configured to receive base station data; and at least oneprocessor configured to execute the one or more instructions stored inthe memory to: divide the base station data into a plurality of piecesof base station data according to a first time unit; generate first dataof the first time unit by superimposing the plurality of pieces of basestation data on each other; divide the first data of the first time unitinto a plurality of second time interval base station data, according toa second time interval unit; calculate at least one probability densityfunction for each second time interval base station data of theplurality of second time interval base station data; generate at leastone piece of first representative data by using respective probabilitydensity functions of the plurality of second time interval base stationdata; and train the base station model, based on the at least one pieceof first representative data.
 2. The electronic device of claim 1,wherein the base station data comprises data related to one of aphysical resource block (PRB) usage, an internet protocol (IP)throughput, a number of active user equipments (UEs), and a downlinkvolume.
 3. The electronic device of claim 1, wherein the at least oneprocessor is further configured to execute the one or more instructionsto: generate a histogram for each second time interval of the pluralityof second time interval base station data by performing random samplingon each second time interval of the plurality of second time intervalbase station data; calculate a probability density function for eachsecond time interval of the plurality of second time interval basestation databased on a corresponding histogram of each second timeinterval of the plurality of second time interval base station data;select at least one piece of second representative data for each secondtime interval of the plurality of second time interval base station databy using a corresponding probability density function of each secondtime interval of the plurality of second time interval base stationdata; and generate the at least one piece of first representative databy connecting the at least one piece of second representative data ofeach second time interval of the plurality of second time interval basestation data to each other, according to the plurality of second timeinterval base station data.
 4. The electronic device of claim 3, whereinthe at least one processor is further configured to execute the one ormore instructions to: perform the random sampling for each second timeinterval such that, from among the plurality of second time intervalbase station data, a first number of first samples corresponding tofirst portions of the second time interval base station data is lessthan a second number of second samples corresponding to second portionsof the second time interval base station data, the first portions of thesecond time interval base station data having been recorded earlier thanthe second portions of the second time interval base station data. 5.The electronic device of claim 3, wherein the at least one processor isfurther configured to execute the one or more instructions to select theat least one piece of second representative data for each second timeinterval of the plurality of second time intervals by using a meandeviation and a standard deviation of the corresponding probabilitydensity function of each second time interval of the plurality of secondtime interval base station data.
 6. The electronic device of claim 1,wherein the at least one processor is further configured to execute theone or more instructions to: generate a network digital twin comprisingthe base station model by setting, based on the at least one piece offirst representative data, at least one parameter of the network digitaltwin; output a reward value for each of the at least one piece of firstrepresentative data for each second time interval of the plurality ofsecond time intervals by applying at least one action of the basestation model to an environment of the at least one piece of firstrepresentative data; select a representative reward value for eachsecond time interval of the plurality of second time intervals fromamong the output reward values; and train the base station model toprevent performing an action having a lowest sum of calculated rewardvalues, among the at least one action, by calculating a sum of theselected representative reward values for each second time interval ofthe plurality of second time intervals.
 7. The electronic device ofclaim 6, wherein the at least one processor is further configured toexecute the one or more instructions to select, as the representativereward value for each second time interval of the plurality of secondtime intervals, a lowest reward value for each second time interval ofthe plurality of second time intervals, from among the output rewardvalues.
 8. The electronic device of claim 6, wherein the at least oneaction of the base station model comprises turning on at least one celland turning off the at least one cell, based on at least one of an atleast one of internet protocol (IP) throughput and a physical resourceblock (PRB) usage.
 9. The electronic device of claim 8, wherein: theturning on of the at least one cell comprises, in a case in which the atleast one of the IP throughput and the PRB usage of a first frequencyband is greater than or equal to a preset first value, turning on atleast one first cell of the first frequency band, and the turning off ofthe at least one cell comprises, in a case in which the at least one ofthe IP throughput and the PRB usage of a second frequency band is lessthan or equal to a preset second value, turning off at least one secondcell of the second frequency band.
 10. A method of generatingrepresentative data for training a base station model, the methodcomprising: dividing base station data into a plurality of pieces ofbase station data according to a first time unit; generating first dataof the first time unit by superimposing the plurality of pieces of basestation data on each other; dividing the first data of the first timeunit into a plurality of second time intervals, according to a secondtime interval unit; calculating at least one probability densityfunction for each second time interval of the plurality of second timeintervals; generating at least one piece of first representative data byusing respective probability density functions of the plurality ofsecond time intervals; and training the base station model, based on theat least one piece of first representative data.
 11. The method of claim10, wherein the base station data comprises data regarding one of aphysical resource block (PRB) usage, an internet protocol (IP)throughput, a number of active user equipments (UEs), and a downlinkvolume.
 12. The method of claim 10, wherein: the calculating of the atleast one probability density function comprises: generating a histogramfor each second time interval of the plurality of second time intervalsby randomly sampling the first data of the first time unit; calculatinga probability density function for each second time interval of theplurality of second time intervals based on a corresponding histogram ofeach second time interval of the plurality of second time intervals; thegenerating of the at least one piece of first representative datacomprises: generating at least one piece of second representative datafor each second time interval of the plurality of second time intervalsby using a corresponding probability density function of each secondtime interval of the plurality of second time intervals; and generatingthe at least one piece of first representative data by connecting the atleast one piece of second representative data of each second timeinterval of the plurality of second time intervals to each other,according to the plurality of second time intervals.
 13. The method ofclaim 12, wherein the generating of the histogram comprises performingthe random sampling such that, from among the first data of the firsttime unit, a first number of first samples corresponding to firstportions of the first data is less than a second number of secondsamples corresponding to second portions of the first data, the firstportions of the first data having been recorded earlier than the secondportions of the first data.
 14. The method of claim 12, wherein thegenerating of the at least one piece of second representative datacomprises generating the at least one piece of second representativedata for each second time interval of the plurality of second timeintervals, by using a mean deviation and a standard deviation of thecorresponding probability density function of each second time intervalof the plurality of second time intervals.
 15. The method of claim 10,wherein the training of the base station model comprises: generating anetwork digital twin comprising the base station model by setting, basedon the at least one piece of first representative data, at least oneparameter of the network digital twin; outputting a reward value foreach of the at least one piece of first representative data for eachsecond time interval of the plurality of second time intervals byapplying at least one action of the base station model to an environmentof the at least one piece of first representative data; selecting arepresentative reward value for each second time interval of theplurality of second time intervals from among the output reward values;and training the base station model to prevent performing an actionhaving a lowest sum of calculated reward values, among the at least oneaction, by calculating a sum of the selected representative rewardvalues for each second time interval of the plurality of second timeintervals.
 16. The method of claim 15, wherein the selecting of therepresentative reward value for each second time interval of theplurality of second time intervals comprises selecting, as therepresentative reward value for each second time interval of theplurality of second time intervals, a lowest reward value for eachsecond time interval, from among the output reward values.
 17. Themethod of claim 15, wherein the at least one action of the base stationmodel comprises turning on at least one cell and turning off the atleast one cell, based on at least one of an internet protocol (IP)throughput and a physical resource block (PRB) usage.
 18. The method ofclaim 17, wherein: the turning on of the at least one cell comprises, ina case in which the at least one of the IP throughput and the PRB usageof a first frequency band is greater than or equal to a preset firstvalue, turning on at least one first cell of the first frequency band,and the turning off the at least one cell comprises, in a case in whichthe at least one of the IP throughput and the PRB usage of a secondfrequency band is less than or equal to a preset second value, turningoff at least one second cell of the second frequency band.
 19. Anon-transitory computer-readable recording medium having recordedthereon a program for generating representative data for training a basestation model that, when executed by at least one processor of a device,causes the device to: divide base station data into a plurality ofpieces of base station data according to a first time unit; generatefirst data of the first time unit by superimposing the plurality ofpieces of base station data on each other; divide the first data of thefirst time unit into a plurality of second time intervals, according toa second time interval unit; calculate at least one probability densityfunction for each second time interval of the plurality of second timeintervals; generate at least one first representative data by usingrespective probability density functions of the plurality of second timeintervals; and train the base station model, based on the at least onefirst representative data.
 20. The non-transitory computer-readablerecording medium of claim 19, wherein the program, when executed by theat least one processor, further cause the device to: generate ahistogram for each second time interval of the plurality of second timeintervals by randomly sampling the first data of the first time unit;calculate a probability density function for each second time intervalof the plurality of second time intervals based on a correspondinghistogram of each second time interval of the plurality of second timeintervals; generate at least one second representative data for eachsecond time interval of the plurality of second time intervals by usinga corresponding probability density function of each second timeinterval of the plurality of second time intervals; and generate the atleast one first representative data by connecting the at least onesecond representative data of each second time interval of the pluralityof second time intervals to each other, according to the plurality ofsecond time intervals.